Brain Tissue Microstructure Characterization Using dMRI Based Autoencoder Neural-Networks

Mauro Zucchelli (1, 2) , Samuel Deslauriers-Gauthier (2) , Rachid Deriche (2)

1 COMUE UCA - COMUE Université Côte d'Azur (2015-2019)
2 ATHENA - Computational Imaging of the Central Nervous System

In recent years, multi-compartmental models have been widely used to try to characterize brain tissue microstructure from Diffusion Magnetic Resonance Imaging (dMRI) data. One of the main drawbacks of this approach is that the number of microstructural features needs to be decided a priori and it is embedded in the model definition. However, the number of microstructural features which is possible to obtain from dMRI data given the acquisition scheme is still not clear. In this work, we aim at characterizing brain tissue using autoencoder neural networks in combination with rotation-invariant features. By changing the number of neurons in the autoencoder latent-space, we can effectively control the number of microstructural features that we obtained from the data. By plotting the autoencoder reconstruction error to the number of features we were able to find the optimal trade-off between data fidelity and the number of microstructural features. Our results show how this number is impacted by the number of shells and the bvalues used to sample the dMRI signal. We also show how our technique paves the way to a richer characterization of the brain tissue microstructure in-vivo.

Mots clés: Spherical Harmonics; Rotation Invariant Features; Neural Networks; Diffusion MRI; Autoencoder; OPAL-Meso

Domaines: Informatique [cs]; Imagerie médicale

Mauro Zucchelli, Samuel Deslauriers-Gauthier, Rachid Deriche. Brain Tissue Microstructure Characterization Using dMRI Based Autoencoder Neural-Networks. MICCAI 2021 - 24th International Conference on Medical Image Computing and Computer Assisted Intervention, Oct 2021, Strasbourg, France. ⟨10.1007/978-3-030-87615-9_5⟩. ⟨hal-03312453⟩